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SAEHD: ‘Face style power’ and ‘Background style power’ are now available for whole_face

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New help messages for these options:

Face style power
Learn the color of the predicted face to be the same as dst inside mask.
If you want to use this option with 'whole_face' you have to use XSeg trained mask.
Warning: Enable it only after 10k iters, when predicted face is clear enough to start learn style.
Start from 0.001 value and check history changes.
Enabling this option increases the chance of model collapse

Background style power
      Learn the area outside mask of the predicted face to be the same as dst.
If you want to use this option with 'whole_face' you have to use XSeg trained mask.
This can make face more like dst.
Enabling this option increases the chance of model collapse. Typical value is 2.0
This commit is contained in:
Colombo 2020-03-30 19:46:17 +04:00
parent 9a9b7e4f81
commit 3702531898
1 changed files with 2 additions and 3 deletions
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5
models/Model_SAEHD/Model.py
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@ -102,9 +102,8 @@ class SAEHDModel(ModelBase):
else: else:
self.options['true_face_power'] = 0.0 self.options['true_face_power'] = 0.0
if self.options['face_type'] != 'wf': self.options['face_style_power'] = np.clip ( io.input_number("Face style power", default_face_style_power, add_info="0.0..100.0", help_message="Learn the color of the predicted face to be the same as dst inside mask. If you want to use this option with 'whole_face' you have to use XSeg trained mask. Warning: Enable it only after 10k iters, when predicted face is clear enough to start learn style. Start from 0.001 value and check history changes. Enabling this option increases the chance of model collapse."), 0.0, 100.0 )
self.options['face_style_power'] = np.clip ( io.input_number("Face style power", default_face_style_power, add_info="0.0..100.0", help_message="Learn to transfer face style details such as light and color conditions. Warning: Enable it only after 10k iters, when predicted face is clear enough to start learn style. Start from 0.001 value and check history changes. Enabling this option increases the chance of model collapse."), 0.0, 100.0 ) self.options['bg_style_power'] = np.clip ( io.input_number("Background style power", default_bg_style_power, add_info="0.0..100.0", help_message="Learn the area outside mask of the predicted face to be the same as dst. If you want to use this option with 'whole_face' you have to use XSeg trained mask. For whole_face you have to use XSeg trained mask. This can make face more like dst. Enabling this option increases the chance of model collapse. Typical value is 2.0"), 0.0, 100.0 )
self.options['bg_style_power'] = np.clip ( io.input_number("Background style power", default_bg_style_power, add_info="0.0..100.0", help_message="Learn to transfer background around face. This can make face more like dst. Enabling this option increases the chance of model collapse. Typical value is 2.0"), 0.0, 100.0 )
self.options['ct_mode'] = io.input_str (f"Color transfer for src faceset", default_ct_mode, ['none','rct','lct','mkl','idt','sot'], help_message="Change color distribution of src samples close to dst samples. Try all modes to find the best.") self.options['ct_mode'] = io.input_str (f"Color transfer for src faceset", default_ct_mode, ['none','rct','lct','mkl','idt','sot'], help_message="Change color distribution of src samples close to dst samples. Try all modes to find the best.")
self.options['clipgrad'] = io.input_bool ("Enable gradient clipping", default_clipgrad, help_message="Gradient clipping reduces chance of model collapse, sacrificing speed of training.") self.options['clipgrad'] = io.input_bool ("Enable gradient clipping", default_clipgrad, help_message="Gradient clipping reduces chance of model collapse, sacrificing speed of training.")